Title

Dr. Kenneth Brown Testifies Before The Consumer Product Safety Commission On The Use Of Chromated Copper Arsenate In Playground

My name is Kenneth Brown. I hold a Ph.D. in mathematical statistics from Johns Hopkins University. I have published numerous articles on statistics and applications to risk assessment. With respect to arsenic in drinking water, I have served on committees (NRC/NAS subcommittee on arsenic in drinking water, Arsenic Task Force of the Society for Environmental Geochemistry and Health), workshops on research needs (NCI/NIEHS/EPA, American Water Works Association), drafted the position paper of the American Council on Science and Health, presented invited and contributed papers at numerous conferences, and co-authored 12 articles - 5 in conference proceedings and 7 in refereed journals, including 2 which were invited. Research has been supported by U.S. EPA, industry, and trade associations (e.g., American Water Works Association).

The CPSC claims that it has dealt with sources of uncertainty and variability, but that is not quite accurate. They have considered some sources of variability within the context of their analysis, which is laudatory, but “uncertainty refers to lack of knowledge in the underlying science” (NRC2, 01, p.109). The CPSC staff needs to consider the credibility of some of its assumptions, which are sources of uncertainty to be identified and addressed openly. Questionable assumptions that are made with good faith, but little or no discussion, tend to lead the trusting but unwary reader to an unfounded level of comfort with the validity of the analysis. Two such assumptions will be discussed. The first is CPSC’s equating limited and intermittent exposure to arsenic in the first few years of life to a chronic lifetime low-level exposure with equivalent total arsenic intake. The second concerns the NRC risk assessment where all persons within a given village were treated as if exposed to the same concentration of arsenic in drinking water, specifically the median concentration of wells tested within the village. Both assumptions are the result of genuine limitations of the science, or the available data, but they still undermine the credibility of the NRC risk assessment and CPSC’s extrapolation of its results to estimation of children’s risk from contact with CCA-treated wood.

The first assumption implies that estimated lifetime cancer risk is based only on total cumulative lifetime arsenic intake, regardless of how it is distributed over the lifetime. The Taiwan data, however, don’t support that assumption. Although flawed with regard to exact exposure levels, the data indicate that duration of exposure (number of years) is more important than daily intake in determining cancer risk. This suggests that long-term exposure, or an unexplained age effect, is having a substantial impact on estimates of lifetime risk of cancer. Neither would apply, however, for arsenic exposure during childhood alone. It follows that the effect of the assumption made in the CPSC procedure would be to overestimate the effect of early-life exposure to arsenic on lifetime cancer risk. It is not clear,however, that even lifetime daily intakes at the low arsenic levels experienced by children coming into contact with CCA-treated wood would pose an increased cancer risk.

The principal argument for low-dose linear cancer risk, and hence risk at extremely low arsenic concentrations, as assumed in the NRC reports and by the CPSC staff, is more a matter of policy than science. Arsenic does not appear to act directly on DNA, the main argument for low-dose linearity. As noted in NRC1 (p.7), “Of the several modes of action that are considered most plausible, a sublinear dose-response curve in the low-dose range is predicted, although linearity cannot be ruled out”. Arsenic is ubiquitous and is possibly even beneficial in small quantities. Such evidence is indirect, based on animal experiments that found arsenic may be nutritionally essential (the two NRC reports emphasized the lack of direct evidence for humans but the EPA risk assessment forum of 1986 (EPA, 1986) considered it more seriously). The point is that even if the risk at very low concentrations in drinking water were reliable (to be discussed next), extrapolation of risk estimates based on chronic exposure to children who are intermittently exposed to CCA-treated wood in childhood is speculative. As a practical example, the assumptions being made in the CPSC analysis about the risk from arsenic would not apply, for example, to tobacco smoke. Tobacco smoke contains hundreds of compounds including at least 40 known carcinogens, and one might speculate that they would probably cover most modes-of-action for chemical carcinogenesis. It is known, however, that the risk of lung cancer diminishes with time, almost to that of a never-smoker, if exposure (smoking) is terminated.

The second assumption to be discussed concerns the uncertainty in the risk assessments of the NRC and U.S. EPA for cancer from arsenic in drinking water. There is uncertainty in all risk assessment, but in this case it was assumed that all the study subjects within a given village were exposed to the same arsenic concentration in drinking water, i.e., they were treated as if they all drank from a single source with the arsenic concentration at the median value of the wells tested within the village. Wells within the same village, however, often differed dramatically in arsenic concentrations. Figure 1, showing the arsenic concentrations by village, for villages with more than one well, was constructed from Table A10-1 of the first NRC report (NRC1, 1999). The same data were analyzed more fully in the article by Morales et al. (2000) that was cited heavily in the second NRC report and in the EPA report.

The first village listed in Figure 1, O-G, had a relatively large number of cancer occurrences. All the recorded cancers for the village were treated as having occurred at exposure concentrations of 30 μg/L. There were five wells, however, tested at 10, 10, 30, 259, and 770 μg/L. What is missing from the data is the distribution of the population in the village across wells, i.e., how many used each well, and, the distribution of the cancer cases across wells, i.e., the number of cancer cases at each well concentration. Not all villages are so extreme, but it is readily apparent from the table that the example just described is not an isolated case. The potential for serious exposure misclassification is obviously high. The effect of such data on risk estimation is apparent in a diagram in which different dose-response models were fit to the data. First, however, it maybe useful to see an example of a model fit to good dose-response data.

The data in Figure 2 are from mortality of rats exposed to hydrogen sulfide, and are used here strictly for illustration, with a logistic model fit to the data. A statistical measure of the goodness of fit, or something such as the AIC (Akaike Information Criteria) used by the NRC to compare different alternatives, is not adequate by itself; it is necessary to graphically examine the fit of the data. In this case, it is apparent graphically that the model describes the data well – the data are close to the curve and predicted values calculated from the curve should be reasonable. Another model might fit the data about equally well, but to do so it is clear that it would have to be very close to the current curve. Thus one can have some level of comfort in using the fitted curve to estimate risk at arbitrary exposure values that may not have been actually observed.

By contrast, several different models were statistically fit to the Taiwan data, using no comparison population (a choice favored by EPA and the EPA Science Advisory Board), and using either the southwest region of Taiwan or all of Taiwan as a comparison population for the study area (NRC2 favored the southwest region). The results are displayed in Figure 3, which appeared in Morales et al. (2000) and NRC2. It is clear that the data are so variable that none of the models provide a good fit to the data. One point near the center of the exposure range is exceedingly high, suggesting that it might be an outlier. More than one model has about the same AIC value, indicating that they cannot be distinguished on a statistical measure of fit (the AIC provides a relative comparison of fits – no statistical measure of fit was found). As one can see graphically, the estimated risks very close to the origin vary widely for different models, so there is considerable model sensitivity.

Nevertheless, the NRC settled on one of the models using the southwestern Taiwanese region as the comparison group, and concluded that the model “provides a satisfactory fit to the epidemiological data and represents a reasonable model choice for use in arsenic risk assessment” (NRC2, p.175). It is hard to see how that statement would be justified even if the data were reliable. Given what is undoubtedly a high error rate in exposure classification in the data, there would be little basis for much credence in any model fit to the data.

What is the NRC’s conclusion about the Southwest Taiwan database? That depends on whether you read NRC1 or NRC2, and whether you read the executive summary or the body of the report. The NRC2 executive summary states “There is a sound database on the carcinogenic effects of arsenic in humans that is adequate for the purposes of a risk assessment.” The NRC1 executive summary, however, makes the recommendation that “Additional epidemiological evaluations are needed to characterize the dose-response relationship for arsenic-associated cancer and noncancer end points, especially at low doses. Such studies are of critical importance for improving the scientific validity of risk assessment.” In the body of NRC1, it is noted that “in some cases, arsenic concentrations varied considerably in different wells within the same village (see Addendum). Hence, there is considerable uncertainty in the data” (p. 274). Morales et al. (2000) commented that exposure is measured at the village level, and that there appears to be variability in the exposure assessment, causing high variability in the risk estimates.

Of the two sources of uncertainty described above, the first addressed an assumption that CPSC needed to make to extrapolate cancer risk estimates based on chronic exposure to intermittent childhood exposure from contact with CCA-treated wood products, given that the estimates for chronic exposure to low arsenic concentrations in drinking water are valid and reliable. The second source of uncertainty addressed the limitations of the data for making valid and reliable estimates at low arsenic concentrations in drinking water. The value of CPSC’s objective is not in question, but it is unrealistic in view of limitations regarding epidemiological data and the mode-of-action of arsenic carcinogenicity.

As an aside, I drafted a position paper on the risk of cancer from arsenic in drinking water in the U.S. for the American Council on Science and Health. They had it heavily reviewed and then wrote their own conclusion. It was submitted, by invitation, to Regulatory Toxicology and Pharamacology where it was peer reviewed again before publication. The conclusion, with which I agree, is that at several hundred μg/L there is clear evidence of cancer and non-cancer effects, but at or below 50 μg/L, limitations regarding the epidemiological data and the mode-of-action of arsenic toxicity are inadequate to support the conclusion that there are adverse health effects in the United States. The implications for the CPSC analysis is that they are trying to ferret out cancer risks at extremely small arsenic intakes for which it is not at all clear that there even is a cancer risk.